Machine learning systems are not just "traditional systems with models added." They introduce fundamentally new failure modes, testing requirements, and operational challenges. Understanding these differences is crucial for building reliable ML products.
Traditional software engineering practices don't directly apply to ML systems. You need new tools, processes, and mindsets to handle the unique challenges of probabilistic systems that depend on data quality.
Core Machine Learning Components
Key Insight: Unlike traditional programming where we write rules, in ML we provide examples (data) and let the algorithm discover patterns. The model learns to generalize from these examples to make predictions on new, unseen data.
Supervised Learning: Learning from Examples
Supervised learning is like teaching by example. You show the model many examples of inputs paired with correct outputs, and it learns to make predictions on new inputs.
House Price Prediction (Regression)
Input Features (X):
- • Square footage: 2,000 sq ft
- • Number of bedrooms: 3
- • Location: Downtown
- • Year built: 1995
Target Output (y):
The ML Training Process
1. Data Collection & Preparation
Gather examples with input features and correct answers. Quality and diversity of data determines model success.
# Example: House price dataset
data = {
'sqft': [2000, 1500, 2500, 1800],
'bedrooms': [3, 2, 4, 3],
'location': ['downtown', 'suburb', 'downtown', 'rural'],
'price': [450000, 320000, 580000, 380000] # Target values
}⚡ Quick Decision
Start with Traditional When:
- • Rules-based solution possible
- • Deterministic outcomes required
- • Small, stable datasets
Consider ML When:
- • Pattern recognition needed
- • Large datasets available
- • Human judgment expensive
Avoid ML When:
- • No clear success metrics
- • Insufficient data
- • High-stakes decisions only